Such applications require characterization of insights across numerous user devices for personalization, and collaborative on-device deep learning becomes necessary. Federated Learning (FL) is a decentralized method of training neural networks by securely sharing model updates with a server without the need to transfer sensitive local user data [1]}, [2]}. On-device federated learning has been an active area of research addressing challenges on secure communication protocols, optimization, privacy-preserving networks, etc. [3]}, [4]}. However, handling new/unseen classes in local devices and training them in an FL setting for the global model to possess characteristics of the new classes is a challenging task, since data transfer from local device to server and vice versa is not feasible. Moreover, the new class information of one user is not known among the other users as well, hence the new classes could be similar or different between the users. In addition, there are multiple statistical heterogeneities like model heterogeneities (ability of end-users to architect their own local models), label heterogeneities and non-IIDness across various communication rounds/FL iterations (disparate data and label distributions across devices).
